1. Field
The present disclosure relates to the field of image processing technologies, and in particular to an abandoned object detection apparatus and method and a system.
2. Description of the Related Art
With the development of computer image processing technology, video surveillance system is widely used in intelligent transportation field. However, most researches concentrate on traffic flow detection, pedestrian tracking, violation vehicle detection, etc., and illegal road occupation recognition is not yet complete. Illegal occupation mainly includes illegal stall, stranded suspicious object, unlawful parking, etc. It can cause traffic accidents if these situations are not disposed in time. But in reality most illegal occupation inspections are by manual work, which need policemen to collect lane information from fixed points regularly. Then it will lead to waste of financial and human cost.
The research on intelligent surveillance includes target detection, target tracking, action recognition. And the target detection is the basic of the whole system. It can be achieved mainly by foreground segmentation on target area, and common detection methods are optical flow method, frame difference algorithm, background modeling approach, etc. The optical flow method extracts moving target by an optical flow feature, has large computation and is sensitive to noise. The frame difference algorithm can handle dynamic scene effectively by time-domain subtraction of two or three neighbor frames. And the background modeling approach segments the moving target by subtracting the current frame image and the background model and has quick calculating speed and usually can provide complete feature data.
The present methods for abandon target detection are split into two categories: non-automatic monitoring algorithm and tracking algorithm. The non-automatic monitoring algorithm includes study-based algorithm and manually setting background algorithm. The first algorithm has special requirements for features of abandon objects, so the abandon objects having different features from training samples cannot be detected accurately. The second algorithm has more limitations because empty background cannot be set under complex scene and it cannot adapt the change of light conditions. And the tracking algorithm track still targets based on background modelling. It heavily depends on effect of background model and easy to be affected by bad phenomena such as a ghost. The tracking method also has high complexity and unsatisfactory performance for long-time abandon objects.
It should be noted that the above description of the background is merely provided for clear and complete explanation of the present disclosure and for easy understanding by those skilled in the art. And it should not be understood that the above technical solution is known to those skilled in the art as it is described in the background of the present disclosure.